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Open AccessLetter

Cohesion Intensive Deep Hashing for Remote Sensing Image Retrieval

1
College of Oceanography and Space Informatics, China University of Petroleum (East China), 66 Changjiang West Road, Qingdao 266580, China
2
School of Computer Science and Engineering, Beihang University, Beijing 100191, China
3
School of Computing, National College of Ireland, 1 D01 K6W2 Dublin, Ireland
4
Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(1), 101; https://doi.org/10.3390/rs12010101
Received: 28 October 2019 / Revised: 22 December 2019 / Accepted: 25 December 2019 / Published: 27 December 2019
(This article belongs to the Section Remote Sensing Image Processing)
Recently, the demand for remote sensing image retrieval is growing and attracting the interest of many researchers because of the increasing number of remote sensing images. Hashing, as a method of retrieving images, has been widely applied to remote sensing image retrieval. In order to improve hashing performance, we develop a cohesion intensive deep hashing model for remote sensing image retrieval. The underlying architecture of our deep model is motivated by the state-of-the-art residual net. Residual nets aim at avoiding gradient vanishing and gradient explosion when the net reaches a certain depth. However, different from the residual net which outputs multiple class-labels, we present a residual hash net that is terminated by a Heaviside-like function for binarizing remote sensing images. In this scenario, the representational power of the residual net architecture is exploited to establish an end-to-end deep hashing model. The residual hash net is trained subject to a weighted loss strategy that intensifies the cohesiveness of image hash codes within one class. This effectively addresses the data imbalance problem normally arising in remote sensing image retrieval tasks. Furthermore, we adopted a gradualness optimization method for obtaining optimal model parameters in order to favor accurate binary codes with little quantization error. We conduct comparative experiments on large-scale remote sensing data sets such as UCMerced and AID. The experimental results validate the hypothesis that our method improves the performance of current remote sensing image retrieval. View Full-Text
Keywords: remote sensing image retrieval; deep hashing; residual net; cohesion intensive; gradualness optimization remote sensing image retrieval; deep hashing; residual net; cohesion intensive; gradualness optimization
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MDPI and ACS Style

Han, L.; Li, P.; Bai, X.; Grecos, C.; Zhang, X.; Ren, P. Cohesion Intensive Deep Hashing for Remote Sensing Image Retrieval. Remote Sens. 2020, 12, 101.

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